class_docstr: Pyramid Scene Parsing Network
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  aux : bool
      Auxiliary loss.
  
  
  Reference:
  
      Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia.
      "Pyramid scene parsing network." *CVPR*, 2017
code: def hybrid_forward(self, F, x):
      c3, c4 = self.base_forward(x)
      outputs = []
      x = self.head(c4)
      x = F.contrib.BilinearResize2D(x, **self._up_kwargs)
      outputs.append(x)
  
      if self.aux:
          auxout = self.auxlayer(c3)
          auxout = F.contrib.BilinearResize2D(auxout, **self._up_kwargs)
          outputs.append(auxout)
      return tuple(outputs)
  
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from .segbase import SegBaseModel
  from .fcn import _FCNHead
label_desc: Use image node_id 2 for calling the PSPNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: tuple